SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 2326–2350 of 4856 papers

TitleStatusHype
Enhancing Interpretability and Generalizability in Extended Isolation ForestsCode0
Knowledge Distillation for Anomaly Detection—0
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection—0
A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection—0
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection—0
Exploring the Potential of Multi-Modal AI for Driving Hazard PredictionCode0
Large Language Models for Spatial Trajectory Patterns Mining—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
A Marketplace Price Anomaly Detection System at Scale—0
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification—0
FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge—0
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly DetectionCode0
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering—0
A Prototype-Based Neural Network for Image Anomaly Detection and LocalizationCode0
Improving Vision Anomaly Detection with the Guidance of Language ModalityCode0
Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection—0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
Expert enhanced dynamic time warping based anomaly detection—0
Self-supervised Learning for Anomaly Detection in Computational Workflows—0
Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
MFL Data Preprocessing and CNN-based Oil Pipeline Defects Detection—0
Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks—0
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous AttentionCode0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified